What is DeepForest?#

DeepForest is a python package for training and predicting ecological objects in airborne imagery. DeepForest currently comes with a tree crown object detection model and a bird detection model. Both are single class modules that can be extended to species classification based on new data. Users can extend these models by annotating and training custom models.


How does deepforest work?#

DeepForest uses deep learning object detection networks to predict bounding boxes corresponding to individual trees in RGB imagery. DeepForest is built on the object detection module from the torchvision package and designed to make training models for ecological object detection simpler.

For more about the motivation behind DeepForest, see some recent talks we have given on computer vision for ecology and practical applications to machine learning in environmental monitoring.

Airborne Ecology

Practical Intro to Computer Vision in Ecology Research

Where can I get help, learn from others, and report bugs?#

Given the enormous array of forest types and image acquisition environments, it is unlikely that your image will be perfectly predicted by a prebuilt model. Below are some tips and some general guidelines to improve predictions.

Get suggestions on how to improve a model by using the discussion board. Please be aware that only feature requests or bug reports should be posted on the issues page.


Free software: MIT license

Why DeepForest?#

Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. Individual crown delineation has been a long-standing challenge in remote sensing and available algorithms produce mixed results. DeepForest is the first open source implementation of a deep learning model for crown detection. Deep learning has made enormous strides in a range of computer vision tasks but requires significant amounts of training data. By including a trained model, we hope to simplify the process of retraining deep learning models for a range of forests, sensors, and spatial resolutions.


Weinstein, B.G.; Marconi, S.; Bohlman, S.; Zare, A.; White, E. Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens. 2019, 11, 1309